def mk_encoder(quality = 25, lowest = -100, highest=60):
"""
Inputs:
- quality_level : int (between 1 and 100)
Returns:
- an encoder from k-length bit strings to
quantized 8x8 DCT matrices
"""
codec = JPEG()
quant = codec.scaled_luminance_quant_matrix(quality)
quant_values = np.ravel(quant)
quant_shape = quant.shape
sorted_indices = np.argsort(quant_values)
bits_per_bin = np.zeros_like(quant_values, dtype='int')
for i,idx in enumerate(sorted_indices):
q = float(quant_values[idx])
value_range = int(highest/q)*q - int(lowest/q)*q
if value_range > 0:
nbits = int(np.ceil(np.log2(float(value_range) / q)))
else:
nbits = 0
bits_per_bin[idx] = int(nbits)
total_nbits = np.sum(bits_per_bin)
encoder = BitEncoder(
total_nbits,
quant,
bits_per_bin,
lowest,
highest)
return encoder
def main(argv):
# discretize_channel(9)
# discretize_channel(10)
vals = discretize_channel(9)
print vals
rand_block = random_11_block(vals)
print rand_block
dctd = two_dim_DCT(rand_block)
print 'DCTd'
print_8_by_8(dctd)
jpeg = JPEG()
print
# For visualizing the quantization matrices.
#
# for quality in range(5, 96):
# quantized = jpeg.scaled_luminance_quant_matrix(quality)
# with open('quant_%s.log' % quality, 'w') as fh:
# for row in range(8):
# for col in range(8):
# fh.write('%d %d %d\n' % (row, col, quantized[row,col]))
quantized = jpeg.luminance_quantize(dctd, 75)
print_8_by_8(quantized)
print 'Dequanitzed'
dequantized = jpeg.luminance_dequantize(quantized, 75)
print_8_by_8(dequantized)
idctd = two_dim_DCT(dequantized, False)
print 'iDCT'
print_8_by_8(idctd)
print 'Diff'
print_8_by_8( idctd - rand_block)
print 'FS'
dithered = jpeg.fs_dither(idctd)
print_8_by_8(dithered)
print 'Diff Dither'
print_8_by_8( dithered - rand_block)
